Predicting Alzheimer’s disease using combined imaging-whole
genome SNP data
Dehan Kong*, Ph.D, K. S. Giovanello†,§, Ph.D, Y.L. Wang‖, Ph.D, Weili Lin‡,§, Ph.D, Eunjee
Lee ¶, M.S., Yong Fan**, Ph.D P. Murali Doraiswamy††,MD, and Hongtu Zhu*,‡,§,, Ph.D
for the Alzheimer’s Disease Neuroimaging Initiative
* Department of Biostatistics, UNC
† Department of Psychology, UNC
‡ Department of Radiology, UNC
§ Biomedical Research Imaging Center, UNC
¶ Department of Statistics, UNC
‖ School of Computing, Informatics, and Decision Systems Engineering, Arizona State
University
** Department of Radiology, University of
Pennsylvania
†† Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University
Correspondence to: Hongtu Zhu.
Address: 3105-C McGavran-Greenberg Hall,
UNC Gillings School of Global Public Health,
135 Dauer Drive,
Campus Box 7420,
Chapel Hill, 27599-7420,
USA
Fax: 919-966-3804; phone: 919-966-7272
Email: htzhu@email.unc.edu
Author disclosures are listed at the end of the paper.
The first two authors, Drs. Kong and Giovanello contributed equally to this paper. The last two
authors, Drs. Doraiswamy and Zhu, are senior authors of this paper.
ABSTRACT
The growing public threat of Alzheimer’s disease (AD) has raised the urgency to discover and
validate prognostic biomarkers. No prior study, to our knowledge, has examined the predictive
value of whole genome SNP (from all 23 chromosomes) data either alone or in combination with
high resolution whole brain MR data. In 343 subjects with mild cognitive impairment (MCI)
enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1), we extracted high
dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration
based hippocampal subregion and surface data), genetic (504,095 SNPs from GWAS),
neurocognitive and clinical data at baseline. MCI patients were followed over 48 months, with
150 participants progressing to AD. Cox regression model and functional principal component
analysis were used to compare the predictive value of combined imaging-genetic markers versus
standard clinical-cognitive markers. Receiver operating characteristic (ROC) analysis revealed
that the model combining just routine clinical and cognitive data with a single genotype (ApoE4)
had a low predictive value at 48 months (AUC 0.75). In contrast, the model combining full
genetic SNP and imaging data (but without any cognitive data) had a much higher predictive
value (AUC 0.95). SNPS on chromosomes 2, 10, 11, 15, 17 and 18 as well as volumes of
hippocampus, amygdala and thalamus contributed significantly. Our findings are the first
demonstration of the value of combined whole brain MR and whole genome SNP data in the 48month prognosis of subjects at risk for AD.
Keywords: Alzheimer , hippocampal surface, mild cognitive impairment, receiver operating
characteristic, whole genome.
Introduction
The growing public threat of Alzheimer’s disease (AD) has raised the urgency to discover and
validate prognostic biomarkers that may identify subjects at greatest risk for future cognitive
decline and accelerate the testing of preventive strategies.1,2 In this regard, studies of
combinatorial biomarkers may have greater ability to capture the heterogeneity and
multifactorial complexity of AD, than a traditional single biomarker study.3
Prior studies of subjects at risk for AD have examined the utility of various individual
biomarkers, such as cognitive tests, fluid markers, imaging measures and some individual
genetic markers (e.g. ApoE4).1 In particular, imaging markers such as hippocampal volume and
shape, cortical regional volumes and thickness, and PET (amyloid imaging, FDG) abnormalities
have all been linked in one or more studies to faster progression in at risk subjects,4-16 but are not
yet optimally predictive at an individual level.
More recently, genome-wide association study (GWAS) data has been used to
characterize several potential genetic risk factors for AD with several cross-sectional studies also
correlating these data with imaging and fluid biomarkers.17 However, no prior study, to our
knowledge, has leveraged both GWAS SNP data, as well as high dimensional whole brain
imaging data to examine their combined value in identifying subjects at greatest risk for
progressing to AD.
Materials and Methods
Alzheimer’s Disease Neuroimaging Initiative
Data used in the preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (www.loni.usc.edu/ADNI). The ADNI was launched
in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging
and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical
companies and non-profit organizations, as a $60 million, 5-year public private partnership. The
primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI),
positron emission tomography (PET), other biological markers, and clinical and
neuropsychological assessment can be combined to measure the progression of mild cognitive
impairment (aMCI) and early Alzheimer’s disease (AD). Determination of sensitive and specific
markers of very early AD progression is intended to aid researchers and clinicians to develop
new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical
trials. The Principal Investigator of this initiative is Michael W. Weiner, M.D., VA Medical
Center and University of California - San Francisco. ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and subjects
have been recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was
to recruit 800 adults, ages 55 to 90, to participate in the research – approximately 200 cognitively
normal older individuals to be followed for 3 years, 400 people with aMCI to be followed for 3
years, and 200 people with early AD to be followed for 2 years. For up-to-date information see
www.adni-info.org.
Data Description
For each subject, the radial distance was obtained from baseline hippocampal surfaces data,
which yields two 15000 dimensional vectors denoting data from left hippocampus and right
hippocampus, respectively. We treated them as realizations of two functional predictors. Next,
we applied functional principal component analysis (FPCA), see Rice and Silverman (1991) and
Ramsay and Silverman (2005) for examples. It turns out that we select 7 functional principal
component (FPC) scores for each predictor, which explain approximately 70% of the variance.
For implementation of FPCA, we use the “svd” function in R software.
Besides the hippocampal surface data, we included several clinical and genetic
covariates: Gender (1=Male; 2=Female), Handedness (1=Right; 2=Left), Marital Status
(1=Married; 2=Widowed; 3=Divorced; 4=Never married), Education length, Retirement (1=Yes;
0=No), and Age. Since Marital Status has four levels, we included three dummy variables. We
also included the APOE4 genotype, which has two alleles, as studies have shown that the
APOE4 genotypes have significant effect in MCI. The first allele has three genotypes: 2, 3, 4,
thus we included two dummy variables for this allele. The second allele has only two genotypes:
3 and 4, so one dummy variable was needed. We also include the ADAS-Cog score, which is a
composite score of 11 questions to reflect the severity of AD.
In addition, we selected ROIs among the 93 ROI volume data from the Jacob atlas.21 We
chose 23 ROIs which may significantly influence MCI progression.10,22,23 The 23 ROIs were
bilateral entorhinal cortices, bilateral hippocampal formation, bilateral amygdala, bilateral
caudate nuclei, bilateral putamen, bilateral posterior limb of internal capsule including cerebral
peduncle, bilateral nucleus accumbens, bilateral lateral ventricles, bilateral thalamus, bilateral
fornix, bilateral cingulate and the corpus callosum.
Finally, we included information from all the 22 chromosomes. Since each chromosome
contains a number of SNPs, we used principal component analysis for each chromosome and
picked the first 2 principal components for each chromosome. We then used the PLINK package
( http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#plink) to perform the principal
component analysis for each chromosome. => We then used the PLINK package (
http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#plink) to perform quality control for the
genomic data. The principal component analysis for each chromosome was conducted using
“svd” function in R software.
Hippocampus Image Preprocessing
We adopted a surface fluid registration based hippocampal subregional analysis package,24
which uses isothermal coordinates and fluid registration to generate one-to-one
hippocampal surface registration for following surface statistics computation. This software
package has been adopted by various studies.25-30
Given the 3D MRI scans, hippocampal substructures were segmented with FIRST31 and
hippocampal surfaces were automatically reconstructed with the marching cube method32. We
applied an automatic algorithm, topology optimization, to introduce two cuts on a hippocampal
surface to convert it into a genus zero surface with two open boundaries. The locations of the two
cuts were at the front and back of the hippocampal surface, representing its anterior junction with
the amygdala, and its posterior limit as it turns into the white matter of the fornix. Then
holomorphic 1-form basis functions were computed.33 These induced conformal grids the
hippocampal surfaces which were consistent across subjects. With this conformal grid, we
computed the conformal representation of the surface,24 i.e., the conformal factor and mean
curvature, which represent the intrinsic and extrinsic features of the surface, respectively. The
“feature image” of a surface was computed by combining the conformal factor and mean
curvature and linearly scaling the dynamic range into [0, 255]. Next, we registered the feature
image of each surface in the dataset to a common template with an inverse consistent fluid
registration algorithm.26 With conformal parameterization, we essentially converted a 3D surface
registration problem into a 2D image registration problem. The flow induced in the parameter
domain establishes high-order correspondences between 3D surfaces. Finally, various surface
statistics were computed on the registered surface, such as multivariate tensor-based
morphometry (mTBM) statistics,33 which retain the full tensor information of the deformation
Jacobian matrix, together with the radial distance,34 which retains information on the
deformation along the surface normal direction.
The SNP data
The subjects’ genotype variables were acquired based on the Human 610-Quad Bead- Chip
(Illumina, Inc., San Diego, CA) in the ADNI database, which resulted in 620,901 SNPs. To
reduce the population stratification effect, we used 749 Caucasians from all 818 subjects with
complete imaging measurements at baseline. Quality control procedures included (i) call rate
check per subject and per SNP marker, (ii) gender check, (iii) sibling pair identification, (iv) the
Hardy-Weinberg equilibrium test, (v) marker removal by the minor allele frequency, and (vi)
population stratification. The second line preprocessing steps include removal of SNPs with (a)
more than 5% missing values, (b) minor allele frequency smaller than 5% , and (c) HardyWeinberg equilibrium p-value < 1e−6. Remaining missing genotype variables were imputed as
the modal value. Seven hundred forty seven subjects and 504,095 SNPs remained.
Statistical Approach
A popular model used in literature is the Cox proportional hazards model (Cox, 1972), which
accounts for other covariates that are associated with the timing of the events. Covariates of
interest include demographic information (8 covariates), the APOE4 genotype (3 covariates), the
ADAS-Cog score (1 covariate), the hippocampus surface data (7 covariates for each curve, total
14 covariates), the ROI volume data (23 covariates), and the chromosome information (2
covariates for each chromosome, total 44 covariates). We used the R function “coxph” to
implement the fitting of the Cox proportional hazards model. We fitted a Cox regression model
with demographic, clinical and cognitive (ADAS-Cog score) predictors as well as APOE,
referred to as the Clinical-Cognitive model (Model 1) from here on, and obtained its estimation
and testing results. This model did not include any other Imaging or GWAS data. We fitted a
second Cox regression model with demographic, Imaging and GWAS predictors, but without the
ADAS-Cog score, referred to as the Imaging-Genetics model (Model 2) from here on, and
obtained its estimation and testing results. We treated the left and right hippocampus surface
data, as well as the SNP data on all 23 chromosomes, as functional predictors. We used FPCA to
extract the first seven FPCs of each of the left and right hippocampus surface data and the first
two FPCs of the SNP data along each chromosome, then used theses as basis functions to
represent the regression coefficient functions associated with the hippocampus surface and SNP
on all chromosomes in the Cox regression. As an illustration, we plotted the first seven FPCs for
both left and right hippocampi in Figure 1.
Conversion: Conversion to dementia was determined according to standardized ADNI criteria
by the clinicians at each site using all available clinical and cognitive information. Among the
343 individuals, 150 MCIs progressed to AD before study completion and the remaining 193
MCIs did not convert to AD prior to study end. MCI converters differed from MCI
nonconverters with respect the APOE 4 genotype and the ADAS-Cog 11 score.
Results
In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer’s
Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging
(volumetric data on 93 brain regions plus a surface fluid registration based hippocampal
subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as
routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48
months, with 150 participants progressing to AD (Online Methods Table 1). MCI converters did
not differ from MCI noncoverters in gender, handedness, marital status, retirement percentage,
and age (p-values>0.05), but as expected, differed from them in APOE4 status as well as
baseline cognition (p-value<0.05) (Table 1). Mean follow up time was 75 days longer in
converters (p=0.06).
Statistical analyses employed a combination of Cox proportional hazard models, receiving
operating characteristic (ROC) methods, and functional principal component analysis (fPCA)
that predict the time of conversion, as well as determine the significant predictors that have
effects on the time to conversion. More specifically, Cox models were used to compute the
association between the hazard rate of the conversion along time (i.e., MCI conversion to AD)
and multimodal predictors (i.e., hippocampal surface area, genotype, and clinical covariates).
For the imaging data, we included both ROI volumes, as well as hippocampus surface
morphology, the latter of which adds additional predicative value. For the genetic data, since
each chromosome contains a number of SNPs, we used principal component analysis for each
chromosome and picked the first 2 principal components for each chromosome. We then used
the PLINK package ( http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#plink) to perform
the principal component analysis for each chromosome. => We then used the PLINK package (
http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#plink) to perform quality control for the
genomic data. The principal component analysis for each chromosome was conducted using
“svd” function in R software.
We compared the predictive value of standard of care (clinical demographic variables,
APOE4, the ADAS-Cog score, Model 1) versus imaging-genetic markers (MRI volumes and
surface data plus GWAS SNP and demographics, Model 2).
Receiver operating characteristic (ROC) analysis revealed that Model 1 (combining just
routine clinical demographics and cognitive data with a single genotype ApoE4) had a low
predictive value at 48 months (AUC 0.75) (Supplemental Table 1, Supplemental Figure 3). In
this model, ApoE4 and ADAS-Cog were the significant predictors. In contrast, Model 2
(combining full genetic SNP and high dimension imaging data with demographics but without
any cognitive data) had a much higher predictive value (AUC 0.95) (Figure 2, Supplemental
Table 2, Supplemental Figure 3). SNPS on chromosome 2, 10, 11, 15, 17 and 18 (Supplemental
Figure 2), ApoE4 status, surface morphology data of both hippocampi (especially anterior
regions, Figure 1, Supplemental Figure 1) and volumes of hippocampus, amygdala and thalamus
contributed significantly. 100-fold cross validation using a test and training data set revealed
AUC of 0.95 (+/-0.014) for the imaging-genetic model and 0.75 (+/-0.024) for the clinicalcognitive model. Combining all variables (cognitive data plus imaging-genetic data) showed
high predictive accuracy (AUC 0.96) but was not different from the value provided by imaginggenetics data alone.
Discussion and Conclusions
These findings are the first demonstration, to our knowledge, of the value of combined
whole brain MR and whole genome SNP data in the 48-month prognosis of subjects at risk for
AD. Our finding support prior MRI studies of volumetric hippocampal changes in prodromal
AD8,18 and extend them by finding that the possible prognostic value of combining information
from high dimensional imaging and genetics may be superior to that provided by routine clinicalcognitive testing data.
Our findings also confirm the association between APOE4 status and AD, and identify
additional new markers on chromosomes 2, 10, 11, 15, 17 and 18 as having significant effects on
conversion. A variety of genes have been identified in prior GWAS studies as potential risk
factors for AD such as clusterin (chromosome 8), complement receptor 1 (chromosome 1),
phosphatidylinositol binding clathrin assembly protein (chromosome 11), sortilin-related
receptor (chromosome 11), triggering receptor expressed on myeloid cells 2 (chromosome 6) and
cluster of differentiation 33 (chromosome 19) as well as TOMM40 (chromosome 19).19 Our
study did not examine any of these newer gene markers specifically but provides support to the
notion that there is additional genetic heritability in late-onset AD beyond that accounted for by
APOE.
There are some strengths and limitations to our analyses. ADNI is a national biomarker
study that utilized rigorous standardized data collection procedures, and well established criteria
to select MCI subjects. Rather than using the individual data on all SNPs, we used the more
conservative statistical method of doing principal component analysis for each chromosome and
picking the first 2 principal components for each chromosome. Our findings survived internal
cross validation but need replication in an independent community based sample. We did not
include measures of pathology (e.g. beta-amyloid) in our models since CSF and amyloid-PET
were available only in a small subset of individuals in ADNI-1. However, a study of ADNI-2
subjects has shown a robust correlation between the APOE e4 ε4 allele and cortical amyloid
burden,20 suggesting that APOE e4 may have served as a surrogate for cortical amyloid plaque
load in our analysis.
Prior investigations of prediction of MCI-AD to AD have utilized feature selection to
assess the most important biomarkers of prediction of conversion. Here, we have demonstrated
the utility of Cox hazard models as a valuable method for identifying the “optimal combination”
of early markers of conversion to AD in patients with MCI. If replicated in independent cohorts,
such combinatorial markers of early AD could be useful for selecting at risk individuals for
prevention trials and for discovering novel targets for discovering disease modifying therapies.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
This work was supported in part by NlH grants RR025747-01, P01CA142538-01, MH086633,
and AG033387 to Dr. Zhu. The content is solely the responsibility of the authors and does not
necessarily represent the official views of the NIH. Data used in preparation of this article were
obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
(adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and
implementation of ADNI and/or provided data but did not participate in analysis or writing of
this report. A complete listing of ADNI investigators can be found at:
http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Data collection and sharing for this project was funded by the Alzheimer's Disease
Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD
ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the
National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering,
and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s
Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company;
Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and
its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen
Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical
Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics,
LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging;
Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health
Research is providing funds to support ADNI clinical sites in Canada. Private sector
contributions are facilitated by the Foundation for the National Institutes of Health
(www.fnih.org). The grantee organization is the Northern California Institute for Research and
Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the
University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro
Imaging at the University of Southern California.
PMD has received research grants and/or advisory fees from several government agencies,
advocacy groups and pharmaceutical/imaging companies. PMD received a grant from ADNI to
support data collection for this study and he owns stock in Sonexa, Maxwell, Adverse Events
and Clarimedix, whose products are not discussed here.
Supplementary information is available at Molecular Psychiatry’s website
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FIGURE LEGENDS
Figure 1. Figure presents the estimated coefficient functions associated with the hippocampus
surface data. Panel (a)-(g) are the estimates of the first seven FPCs corresponding to the sorted
seven eigenvalues, in which panel (a) corresponds the largest eigenvalue, and Panel (h) is the
color bar with 11 lines representing 11 equally spaced points between [-0.0462, 0.0421].
Figure 2. The AUC curves comparison for the imaging plus genetics model versus the cognitive
model plotted using one pair of training and testing data set. The blue solid line denotes the AUC
curve for the image-genetics model and the red dash line denotes the AUC curve for clinicalcognitive model.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 1. Figure presents the estimated coefficient functions associated with the hippocampus
surface data. Panel (a)-(g) are the estimates of the first seven FPCs corresponding to the sorted
seven eigenvalues, in which panel (a) corresponds the largest eigenvalue, and Panel (h) is the
color bar with 11 lines representing 11 equally spaced points between [-0.0462, 0.0421].
Figure2: The AUC curves comparison for the imaging plus genetics model versus the cognitive model
plotted using one pair of training and testing data set. The blue solid line denotes the AUC curve for the
image-genetics model and the red dash line denotes the AUC curve for clinical-cognitive model.
Table 1: Study sample –Comparison of Converters and Nonconverters
MCI converters
MCI
Test statistics
nonconverters
Sample Size
150
Mean Age (in years)
74.7 (7.0)
75.1 (7.5)
T test:
Pvalue=0.69
Gender (Male percentage)
60.0%
66.8%
Chi-square test:
Pvalue=0.23
Handedness (Right hand percentage)
92.0%
90.7%
Chi-square test:
Pvalue=0.81
Marital Status (Widowed percentage)
10.7%
11.9%
Chi-square test:
Pvalue=0.95
Marital Status (Divorced percentage)
4.7%
6.2%
Marital Status (Never Married
percentage)
Mean Education Length (in years)
1.3%
1.0%
15.6 (2.9)
15.8 (3.0)
T test:
Pvalue=0.52
Retirement percentage
80.0%
79.3%
Chi-square test:
Pvalue=0.98
APOE 4 carriers (%) : APOE4 first
allele (genotype 3 percentage)
78.7%
81.4%
Chi-square test:
Pvalue=0.02
APOE4 first allele (genotype 4
percentage)
APOE4 second allele (genotype 4
percentage)
17.3%
9.3%
30.0%
56.0%
Chi-square test:
Pvalue=3 × 10−6
Mean ADAS-Cog 11 Score
13.2 (4.0)
10.2 (4.3)
T test:
Pvalue=1 × 10−10
Mean Duration of Follow up (in days)
1009.9
934.4
T test:
Pvalue=0.06
193